Infrared cameras are utilized in a wide variety of imaging applications to capture infrared radiation from objects as infrared images. For instance, infrared cameras may be utilized for nighttime applications to enhance visibility under low light conditions.
An infrared camera, such as for example mid-wave and long-wave infrared imagers, typically produce data with a 12 to 16 bit dynamic range. Due to limitations in analog video encoders, analog video standards (such as NTSC, PAL), video monitors, digital video standards (such as MPEG4, H.264, etc.) and human perception, the effective dynamic range is usually no greater than 8 bit. Using a strict linear conversion (e.g., keeping only the 8 most significant bits of a 14-bit signal) may result in severe loss of detail information. For this reason, dynamic range compression algorithms have been developed. Some include histogram equalization (HE), contrast limited adaptive histogram equalization (CLAHE), gradient domain methods, and unsharp masking. In some cases, however, these image enhancement techniques have been developed for visible light imagers and, therefore, may not be optimal for infrared imagers.
As a result, there is a need for improved processing techniques for enhancing display of infrared images.